Optimizasyon Teknikleri Kullanılarak Kapalı Alan 802.11ac Kablosuz WLAN Modelleme için Bir Karar Destek Aracı

Year 2020, , 1231 - 1244, 30.09.2020

Mevlüt Ersoy , Tuncay Yiğit , Asım Sinan Yüksel

https://doi.org/10.31202/ecjse.739114

Abstract

Kablosuz Yerel Ağ (WLAN), kapalı alan ve dış ortamlarda kablosuz yayın ile Internet erişimi sağlayan bir teknolojidir. Düşük maliyetli, kullanımı kolay ve yaygın olarak kullanılmaktadır. Bununla birlikte, kapalı alanların karmaşık yapısı nedeniyle kapsamını etkileyen birçok faktör vardır. Bu nedenle, Erişim Noktalarını (AP'ler) kablosuz ağa en güçlü ve en tutarlı kapsama alanını sağlayacak şekilde yerleştirmek gerekir. Bu faktörler WLAN performansını doğrudan etkilediğinden, ağ uzmanları genellikle beklenmedik sorunlarla karşılaşırlar ve bulunduktan sonra erişim noktalarından ölçümler alarak ağ kapsamını deneme yanılma yoluyla optimize etme eğilimindedirler. WLAN optimizasyonu için anten tipi, kanallar, duvar yapıları, verim gibi çeşitli metrikler kullanılabilir. Karar destek aracımız, ağ uzmanlarının bu metrikleri kullanarak bir ağ modeli oluşturmasına yardımcı olur. Kapalı alan ortamlarında AP'lerin kapsamının belirlenmesi doğrusal olmayan bir sorundur. Bu nedenle, yazılım aracımız tavlama ve genetik algoritmanın sezgisel yaklaşımlarına dayanmaktadır. Bu araç aracılığıyla, bir kapalı alana yerleştirilecek optimum AP sayısına karar verilebilir ve optimum ağ kapsama alanı için en iyi AP yerleşimi kolayca elde edilebilir.

Keywords

WLAN Optimizasyonu, Genetik Algoritma, Tavlama Benzetimi

A Decision Support Tool for Indoor 801.11ac WLAN Modeling Using Optimization Techniques

Abstract

Wireless Local Area Network (WLAN) is a technology that provides wireless broadband Internet access in indoor or outdoor environments. It is low cost, easy to use and widely deployed. However, there are many factors that affect its coverage due to the complex structure of indoor settings. For this reason, it is necessary to locate the Access Points (APs) in such a way as to provide the wireless network with the strongest and most consistent coverage. Since these factors directly affect WLAN performance, network specialists often encounter unexpected problems and tend to optimize network coverage by trial and error, taking measurements from access points after they’re located. Several metrics such as antenna type, channels, wall structures, throughput can be used for WLAN optimization. Our decision support tool helps network specialists generate a network model by employing these metrics. Determining the coverage of APs in indoor settings is a nonlinear problem. Therefore, our tool is based on the heuristic approaches of the simulated annealing and genetic algorithm. Via this tool, the optimum number of APs to be placed in an indoor setting can be decided and the best AP placement for optimal network coverage can be easily obtained.

Keywords

WLAN Optimization, Genetic Algorithm, Simulated Annealing

References

• S. V. Azhari, Ö. Gürbüz, O. Ercetin, M. H. Daei, H. Barghi, and M. Nassiri, “Delay sensitive resource allocation over high speed IEEE802.11 wireless LANs,” Wirel. Networks, vol. 26, no. 3, pp. 1949–1968, 2020, doi: 10.1007/s11276-018-1889-7.
• V. K. Jones and H. Sampath, “Emerging technologies for WLAN,” IEEE Commun. Mag., vol. 53, no. 3, pp. 141–149, 2015, doi: 10.1109/MCOM.2015.7060496.
• A. M. Gibney, M. Klepal, and D. Pesch, “A wireless local area network modeling tool for scalable indoor access point placement optimization,” Spring Simul. Multiconference 2010, SpringSim’10, 2010, doi: 10.1145/1878537.1878707.
• M. Kanaan and M. Suveren, “A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture,” Neural Comput. Appl., vol. 28, no. 11, pp. 3603–3615, 2017, doi: 10.1007/s00521-016-2266-z.
• A. Mc Gibney, M. Klepal, and D. Pesch, “Agent-based optimization for large scale WLAN design,” IEEE Trans. Evol. Comput., vol. 15, no. 4, pp. 470–486, 2011, doi: 10.1109/TEVC.2010.2064324.
• S. Bosio, A. Eisenbl¨atter, G. Hans-Florian, S. Iana, and D. Yuan, “Mathematical Optimization Models for WLAN Planning,” in Graphs and Algorithms in Communication Networks, 2010, pp. 283–284.
• B. Wu, J. Luo, and C. Yang, “Wireless sensor network minimum beacon set selection algorithm based on tree model,” Neural Comput. Appl., vol. 30, no. 3, pp. 965–976, 2018, doi: 10.1007/s00521-016-2734-5.
• A. Bahri and S. Chamberland, “On the wireless local area network design problem with performance guarantees,” Comput. Networks, vol. 48, no. 6, pp. 856–866, 2005, doi: 10.1016/j.comnet.2004.11.009.
• D. Plets, W. Joseph, K. Vanhecke, E. Tanghe, and L. Martens, “Coverage prediction and optimization algorithms for indoor environments,” Eurasip J. Wirel. Commun. Netw., vol. 2012, no. December, 2012, doi: 10.1186/1687-1499-2012-123.
• L. Arya and S. C. Sharma, “Coverage and Analysis of Obstructed Indoor WLAN using Simulation Software and Optimization Technique 1,” Conf. Adv. Commun. Control Syst., vol. 2013, no. Cac2s, pp. 631–637, 2013.
• H. T. T. Binh, N. T. Hanh, L. Van Quan, and N. Dey, “Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks,” Neural Comput. Appl., vol. 30, no. 7, pp. 2305–2317, 2018, doi: 10.1007/s00521-016-2823-5.
• S. Kouhbor, J. Ugon, A. Kruger, A. Rubinov, and P. Branch, “A new algorithm for the placement of WLAN access points based on nonsmooth optimization technique,” 7th Int. Conf. Adv. Commun. Technol. ICACT 2005, vol. 1, pp. 352–357, 2005, doi: 10.1109/icact.2005.245869.
• A. H. Muqaibel, N. M. Iya, U. M. Johar, and M. A. Landolsi, “Ultra Wideband Characterization of Through-Wall Propagation Using Transmission and Reflection Measurements,” Arab. J. Sci. Eng., vol. 38, no. 4, pp. 901–911, 2013, doi: 10.1007/s13369-012-0528-3.
• S. Japertas, E. Orzekauskas, and R. Slanys, “Research of IEEE 802.11 standard signal propagation features in multi partition indoors,” Elektron. Ir Elektrotechnika, vol. 19, no. 8, pp. 31–34, 2012.
• M. Lott and I. Forkel, “A multi-wall-and-floor model for indoor radio propagation,” in IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings, 2016, vol. 21, no. 1, pp. 31–42, doi: 10.18820/24150525/comm.v21.3.
• A. Ben Zineb and M. Ayadi, “A Multi-wall and Multi-frequency Indoor Path Loss Prediction Model Using Artificial Neural Networks,” Arab. J. Sci. Eng., vol. 41, no. 3, pp. 987–996, 2016, doi: 10.1007/s13369-015-1949-6.
• H. A. Obeidat et al., “An Indoor Path Loss Prediction Model Using Wall Correction Factors for Wireless Local Area Network and 5G Indoor Networks,” Radio Sci., vol. 53, no. 4, pp. 544–564, 2018, doi: 10.1002/2018RS006536.
• F. Capulli, C. Monti, and M. Vari, “Path Loss Models for IEEE 802.1 a Wireless Local Area Networks,” in 2006 3rd International Symposium on Wireless Communication Systems, 2006, no. 3, pp. 621–624.
• E. E. 300 328, “Wideband transmission systems; Data transmission equipment operating in the 2,4 GHz ISM band and using wide band modulation techniques,” ETSI Stand., vol. 3, pp. 1–85, 2016.
• T. Yigit and M. Ersoy, “Testing and design of indoor WLAN using artificial intelligence techniques,” Elektron. ir Elektrotechnika, vol. 20, no. 6, pp. 154–157, 2014, doi: 10.5755/j01.eee.20.6.7290.
• T. Oka, Y. Sakaguchi, Y. Nagao, M. Kurosaki, and H. Ochi, “Preamble Generation using Genetic Algorithm for MIMO Wireless LAN System,” in The 9th International Conference on Advanced Communication Technology, 2007, pp. 1737–1742.
• X. Du and K. Yang, “A map-assisted wifi ap placement algorithm enabling mobile device’s indoor positioning,” IEEE Syst. J., vol. 11, no. 3, pp. 1467–1475, 2017, doi: 10.1109/JSYST.2016.2525814.
• S. Sakamoto, T. Oda, E. Kulla, M. Ikeda, L. Barolli, and F. Xhafa, “Performance analysis of WMNs using simulated annealing algorithm for different temperature values,” Proc. - 2013 7th Int. Conf. Complex, Intelligent, Softw. Intensive Syst. CISIS 2013, pp. 164–168, 2013, doi: 10.1109/CISIS.2013.34.
• T. Jiang and G. Zhu, “Uniform design simulated annealing for optimal access point placement of high data rate indoor wireless LAN using OFDM,” IEEE Int. Symp. Pers. Indoor Mob. Radio Commun. PIMRC, vol. 3, pp. 2302–2306, 2003, doi: 10.1109/PIMRC.2003.1259129.
• A. 300 S. Data Sheet, “Aruba 300 Series,” vol. 2.